


from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class     __UpperCAmelCase							(       yaml.SafeLoader							):


     """simple docstring"""


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  [self.constructed_objects[key_node] for key_node, _ in node.value]
        __a					=  [tuple(__A    ) if isinstance(__A  ,       __A    ) else key for key in keys]
        __a					=  Counter(__A    )
        __a					=  [key for key in counter if counter[key] > 1]
        if duplicate_keys:
           raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''    )


     def 	snake_case_  (	self  ,       __A  ,       __A=False    ):
        __a					=  super().construct_mapping(__A  ,       deep=__A    )
        self._check_no_duplicates_on_constructed_node(__A    )
        return mapping







def  a   (lowerCAmelCase__   ):
   __a					=  list(readme_content.splitlines()   )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      __a					=  full_content[1:].index("""---"""   ) + 1
      __a					=  """\n""".join(full_content[1:sep_idx]   )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :]   )

   return None, "\n".join(lowerCAmelCase__   )


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     # class attributes
     _lowerCamelCase   =      {"""train_eval_index"""}  # train-eval-index in the YAML metadata


     @classmethod
     def 	snake_case_  (	cls  ,       __A    ):
        with open(__A  ,       encoding="""utf-8"""    ) as readme_file:
           __a	,    __a					=  _split_yaml_from_readme(readme_file.read()    )
        if yaml_string is not None:
           return cls.from_yaml_string(__A    )
        else:
           return cls()


     def 	snake_case_  (	self  ,       __A    ):
        if path.exists():
           with open(__A  ,       encoding="""utf-8"""    ) as readme_file:
              __a					=  readme_file.read()
        else:
           __a					=  None
        __a					=  self._to_readme(__A    )
        with open(__A  ,       """w"""  ,       encoding="""utf-8"""    ) as readme_file:
           readme_file.write(__A    )


     def 	snake_case_  (	self  ,       __A = None    ):
        if readme_content is not None:
           __a	,    __a					=  _split_yaml_from_readme(__A    )
           __a					=  """---\n""" + self.to_yaml_string() + """---\n""" + content
        else:
           __a					=  """---\n""" + self.to_yaml_string() + """---\n"""
        return full_content


     @classmethod
     def 	snake_case_  (	cls  ,       __A    ):
        __a					=  yaml.load(__A  ,       Loader=_NoDuplicateSafeLoader    ) or {}

        # Convert the YAML keys to DatasetMetadata fields
        __a					=  {
            (key.replace("""-"""  ,       """_"""    ) if key.replace("""-"""  ,       """_"""    ) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__A    )


     def 	snake_case_  (	self    ):
        return yaml.safe_dump(
            {
                (key.replace("""_"""  ,       """-"""    ) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }  ,       sort_keys=__A  ,       allow_unicode=__A  ,       encoding="""utf-8"""  ,       ).decode("""utf-8"""    )







SCREAMING_SNAKE_CASE							    =							{
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
  from argparse import ArgumentParser

  SCREAMING_SNAKE_CASE							    =							ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
  ap.add_argument('readme_filepath')
  SCREAMING_SNAKE_CASE							    =							ap.parse_args()

  SCREAMING_SNAKE_CASE							    =							Path(args.readme_filepath)
  SCREAMING_SNAKE_CASE							    =							DatasetMetadata.from_readme(readme_filepath)
  print(dataset_metadata)
  dataset_metadata.to_readme(readme_filepath)




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				"""poolformer"""







   def __init__(     self						:						str   ,   lowerCamelCase_						:						Any=3   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Optional[Any]=3   ,   lowerCamelCase_						:						List[str]=4.0   ,   lowerCamelCase_						:						Tuple=[2, 2, 6, 2]   ,   lowerCamelCase_						:						Optional[Any]=[64, 128, 320, 512]   ,   lowerCamelCase_						:						Tuple=[7, 3, 3, 3]   ,   lowerCamelCase_						:						Optional[Any]=[4, 2, 2, 2]   ,   lowerCamelCase_						:						Dict=[2, 1, 1, 1]   ,   lowerCamelCase_						:						Tuple=4   ,   lowerCamelCase_						:						Optional[int]=0.0   ,   lowerCamelCase_						:						Union[str, Any]="gelu"   ,   lowerCamelCase_						:						Union[str, Any]=True   ,   lowerCamelCase_						:						Union[str, Any]=1E-5   ,   lowerCamelCase_						:						int=0.0_2   ,   **lowerCamelCase_						:						str   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      num_channels
    UpperCamelCase			  =      patch_size
    UpperCamelCase			  =      stride
    UpperCamelCase			  =      padding
    UpperCamelCase			  =      pool_size
    UpperCamelCase			  =      hidden_sizes
    UpperCamelCase			  =      mlp_ratio
    UpperCamelCase			  =      depths
    UpperCamelCase			  =      patch_sizes
    UpperCamelCase			  =      strides
    UpperCamelCase			  =      num_encoder_blocks
    UpperCamelCase			  =      drop_path_rate
    UpperCamelCase			  =      hidden_act
    UpperCamelCase			  =      use_layer_scale
    UpperCamelCase			  =      layer_scale_init_value
    UpperCamelCase			  =      initializer_range
    super().__init__(**lowerCamelCase_				)





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				version.parse("""1.11"""		)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
        ]				)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return 2E-3
